Personalized Course Recommendation Model Based on User’s Interests and Feature Information

To resolve the problems of sparse data and poor recommendation effect in course recommendation, a personalized course recommendation model based on user’s interests and feature information is presented in this study. Firstly, the weight of feature information of the course is defined to reflect the importance of the attributes of the course in the recommendation process. Secondly, the exponential decay function is designed and the time coefficient is integrated to fully adapt to the changes of user interests over time. Finally, the collaborative filtering recommendation scheme is introduced to integrate user interest preferences and attributes to obtain a comprehensive similarity. The experimental results show that the experimental results on the dataset show that the proposed collaborative filtering recommendation model can provide better recommendation quality than traditional algorithms.